In view of the confusion problem of few shot learning in communication signal recognition, a communication signal recognition algorithm is proposed based on semisupervised generative adversarial network in this paper. Firstly, this algorithm utilizes a small amount of labeled samples and a large amount of unlabeled samples for training the network with the idea of semisupervised learning. Secondly, an auxiliary classifier in the output layer is added to determine the result and a new loss function and objective optimization are designed to meet the purpose of generating fake data and realizing signal classification. Finally, in order to reduce the complexity of the algorithm and accelerate the convergence speed of the algorithm effectively, different activation functions are chosen by the network and the deconvolution and Dropout are used to replace the pooling layer. The experimental results show that this algorithm is adaptable and computationally, the recognition accuracy is improved by 6%~13% compared with the traditional algorithm, effectively realizing the modulation pattern recognition in few shot learning.